Online Learning for Large Scale Structured Data in Complex Situations

在线学习（OL）是预测一系列问题答案的过程。近年来，由于具有处理大规模非结构化数据和适应不断变化的环境的天然能力，人工智能备受关注。然而，OL有三个缺点：它不能扩展到结构化数据；它通常假设所有的数据都是同等重要的；它通常认为所有的数据都是完整的和无噪声的。这些弱点限制了它的效用，因为真实的数据，如在处理社交网络中必须分析的数据，欺诈检测不满足限制。这个项目的目的是发展OL的理论和实践进展，克服现有的弱点。

Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are complete and noise-free. These weaknesses limit its utility, because real data such as those that must be analysed in processing social networks, fraud detection do not satisfy the restrictions. The aim of this project is to develop theoretical and practical advances in OL that overcome the existing weaknesses.